Emergency medical services (EMS) are a vital element of the public healthcare system in China,^([1])providing an opportunity to respond to critical medical conditions and save people’s lives.^([2])The accessibility o...Emergency medical services (EMS) are a vital element of the public healthcare system in China,^([1])providing an opportunity to respond to critical medical conditions and save people’s lives.^([2])The accessibility of EMS has received considerable attention in health and transport geography studies.^([3])One of the optimal gauges for evaluating the accessibility of EMS is the response time,which is defined as the time from receiving an emergency call to the arrival of an ambulance.^([4])Beijing has already reduced the response time to approximately12 min,and the next goal is to ensure that the response time across Beijing does not exceed 12 min (the information comes from the Beijing Emergency Medical Center).展开更多
Background: Further strategies are needed to deal with the high losses to suicide. New modalities should be explored within the context of suicide prevention. Aim: The aim of the study was to evaluate participants’ e...Background: Further strategies are needed to deal with the high losses to suicide. New modalities should be explored within the context of suicide prevention. Aim: The aim of the study was to evaluate participants’ experiences of a web based program for mental health care staff, including its potential clinical relevance. Methods: Nineteen participants participated in five focus groups. Data was analyzed using content analysis. Results: The analysis showed participants’ experiences of the program’s contents and format (“Web Based Modules”, “Discussion Groups”) and practical value (“Clinical Relevance and Use”, “Effects on Communication and Climate”). Conclusions: The program partly increased awareness about risk factors and the importance of inquiring about suicide ideation/plans and documenting suicide assessments. Experiences of the clinical value were varying and may be increased through potential enhancements.展开更多
Developers integrate web Application Programming Interfaces(APIs)into edge applications,enabling data expansion to the edge computing area for comprehensive coverage of devices in that region.To develop edge applicati...Developers integrate web Application Programming Interfaces(APIs)into edge applications,enabling data expansion to the edge computing area for comprehensive coverage of devices in that region.To develop edge applications,developers search API categories to select APIs that meet specific functionalities.Therefore,the accurate classification of APIs becomes critically important.However,existing approaches,as evident on platforms like programableweb.com,face significant challenges.Firstly,sparsity in API data reduces classification accuracy in works focusing on single-dimensional API information.Secondly,the multidimensional and heterogeneous structure of web APIs adds complexity to data mining tasks,requiring sophisticated techniques for effective integration and analysis of diverse data aspects.Lastly,the long-tailed distribution of API data introduces biases,compromising the fairness of classification efforts.Addressing these challenges,we propose MDGCN-Lt,an API classification approach offering flexibility in using multi-dimensional heterogeneous data.It tackles data sparsity through deep graph convolutional networks,exploring high-order feature interactions among API nodes.MDGCN-Lt employs a loss function with logit adjustment,enhancing efficiency in handling long-tail data scenarios.Empirical results affirm our approach’s superiority over existing methods.展开更多
The increasing number of available Web Application Programming Interfaces(APIs)in various service sharing communities have enabled software developers to develop their interested multimedia mashups quickly and conveni...The increasing number of available Web Application Programming Interfaces(APIs)in various service sharing communities have enabled software developers to develop their interested multimedia mashups quickly and conveniently.In this situation,a multimedia mashup with complex functionalities could be achieved by composing a set of pre-selected Web APIs by software developers.However,due to the APIs diversity in terms of development organization,programming language,invocation interface,etc,it is often difficult to determine the compatibility between the APIs selected by multimedia mashup developers beforehand especially when the developers have little background knowledge of APIs,which significantly decreases the successful rate of subsequent multimedia mashup development.In response to this challenge,we propose a subgraph matching-based compatible API’s composition recommendation method,called SubMC_(WACR).The advantage of SubMC_(WACR) is that it can directly search for the API’s subgraphs that not only meet the functional requirements of the multimedia mashup but also are compatible with each other,thus boosting the effectiveness of multimedia mashup development.Through extensive experiments on a real dataset crawled from the Web API sharing platform ProgrammableWeb.com,we have demonstrated that our proposed recommendation method achieves significant improvements in terms of recommendation precision and compatibility compared with other competitive API recommendation methods.展开更多
基金supported by National Key Research & Development Program of China (2022YFC3006201)。
文摘Emergency medical services (EMS) are a vital element of the public healthcare system in China,^([1])providing an opportunity to respond to critical medical conditions and save people’s lives.^([2])The accessibility of EMS has received considerable attention in health and transport geography studies.^([3])One of the optimal gauges for evaluating the accessibility of EMS is the response time,which is defined as the time from receiving an emergency call to the arrival of an ambulance.^([4])Beijing has already reduced the response time to approximately12 min,and the next goal is to ensure that the response time across Beijing does not exceed 12 min (the information comes from the Beijing Emergency Medical Center).
文摘Background: Further strategies are needed to deal with the high losses to suicide. New modalities should be explored within the context of suicide prevention. Aim: The aim of the study was to evaluate participants’ experiences of a web based program for mental health care staff, including its potential clinical relevance. Methods: Nineteen participants participated in five focus groups. Data was analyzed using content analysis. Results: The analysis showed participants’ experiences of the program’s contents and format (“Web Based Modules”, “Discussion Groups”) and practical value (“Clinical Relevance and Use”, “Effects on Communication and Climate”). Conclusions: The program partly increased awareness about risk factors and the importance of inquiring about suicide ideation/plans and documenting suicide assessments. Experiences of the clinical value were varying and may be increased through potential enhancements.
基金partially supported by the National Natural Science Foundation of China(No.92267104)the project of Key Science Foundation of Yunnan Province China(No.202101AS070007)the Project of Dou Wanchun Expert Workstation of Yunnan Province,China(No.202205AF1500).
文摘Developers integrate web Application Programming Interfaces(APIs)into edge applications,enabling data expansion to the edge computing area for comprehensive coverage of devices in that region.To develop edge applications,developers search API categories to select APIs that meet specific functionalities.Therefore,the accurate classification of APIs becomes critically important.However,existing approaches,as evident on platforms like programableweb.com,face significant challenges.Firstly,sparsity in API data reduces classification accuracy in works focusing on single-dimensional API information.Secondly,the multidimensional and heterogeneous structure of web APIs adds complexity to data mining tasks,requiring sophisticated techniques for effective integration and analysis of diverse data aspects.Lastly,the long-tailed distribution of API data introduces biases,compromising the fairness of classification efforts.Addressing these challenges,we propose MDGCN-Lt,an API classification approach offering flexibility in using multi-dimensional heterogeneous data.It tackles data sparsity through deep graph convolutional networks,exploring high-order feature interactions among API nodes.MDGCN-Lt employs a loss function with logit adjustment,enhancing efficiency in handling long-tail data scenarios.Empirical results affirm our approach’s superiority over existing methods.
文摘The increasing number of available Web Application Programming Interfaces(APIs)in various service sharing communities have enabled software developers to develop their interested multimedia mashups quickly and conveniently.In this situation,a multimedia mashup with complex functionalities could be achieved by composing a set of pre-selected Web APIs by software developers.However,due to the APIs diversity in terms of development organization,programming language,invocation interface,etc,it is often difficult to determine the compatibility between the APIs selected by multimedia mashup developers beforehand especially when the developers have little background knowledge of APIs,which significantly decreases the successful rate of subsequent multimedia mashup development.In response to this challenge,we propose a subgraph matching-based compatible API’s composition recommendation method,called SubMC_(WACR).The advantage of SubMC_(WACR) is that it can directly search for the API’s subgraphs that not only meet the functional requirements of the multimedia mashup but also are compatible with each other,thus boosting the effectiveness of multimedia mashup development.Through extensive experiments on a real dataset crawled from the Web API sharing platform ProgrammableWeb.com,we have demonstrated that our proposed recommendation method achieves significant improvements in terms of recommendation precision and compatibility compared with other competitive API recommendation methods.
